"""
FastAPI backend for rStar-Math Demonstrator
"""
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List, Dict, Any, Optional
import uvicorn

from src.core.mcts import MCTS
from src.core.ppm import ProcessPreferenceModel
from src.models.model_interface import OpenAIModel, AnthropicModel

app = FastAPI(title="rStar-Math Demonstrator API")

class MathProblem(BaseModel):
    problem_text: str
    model_name: str
    use_rstar: bool = True
    mcts_simulations: int = 1000
    temperature: float = 0.7

class SolutionResponse(BaseModel):
    solution_steps: List[str]
    confidence_score: float
    reasoning_path: List[Dict[str, Any]]
    execution_time: float

@app.post("/solve", response_model=SolutionResponse)
async def solve_problem(problem: MathProblem) -> SolutionResponse:
    """
    Solve a mathematical problem using specified LLM with or without rStar-Math enhancement.
    """
    try:
        # Implementation details to be added
        pass
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/compare-models")
async def compare_models(problem: MathProblem) -> Dict[str, Any]:
    """
    Compare solutions across different LLMs.
    """
    try:
        # Implementation details to be added
        pass
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/generate-integration-code")
async def generate_integration_code(
    framework: str,
    config: Dict[str, Any]
) -> Dict[str, str]:
    """
    Generate integration code for specified framework.
    """
    try:
        # Implementation details to be added
        pass
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

if __name__ == "__main__":
    uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)
